Mobile tag sensing and location estimation

ABSTRACT

Apparatuses, methods, and systems for estimating a location of a tag are disclosed. One method includes identifying a physical shape of a structure or characteristics of the structure, sensing a condition of the structure, generating a first set of weighted likelihoods based on the physical shape of the structure or characteristics of the structure, wherein the first set of weighted likelihoods includes a weighted likelihood of the tag being at each one of a plurality of grid points within the structure, generating a second set of weighted likelihoods based on the sensed condition of the structure, wherein the second set of weighted likelihoods includes a weighted likelihood of the tag being at each one of the plurality of grid points, generating a combined set of likelihoods based on the first set of weighted likelihoods and the second set of weighted likelihoods, and estimating a location of the tag based on the combined set of likelihoods.

RELATED APPLICATIONS

This patent application claims priority to U.S. patent application Ser.No. 15/953,466, filed Apr. 15, 2018 which claims the benefit of U.S.Provisional Patent Application Ser. No. 62/568,032, filed Oct. 4, 2017,which are herein incorporated by reference.

FIELD OF THE EMBODIMENTS

The described embodiments relate generally to building controls. Moreparticularly, the described embodiments relate to location estimation ofa mobile tag within a structure.

BACKGROUND

Intelligent building control provides for control of structure that isuser intelligent. That is, based on behavior or predicted behavior ofusers within the structure, the intelligent building control providesthe users with intelligent environmental controls, safety controls,logistical, and/or information control.

It is desirable to have a method, system and apparatus for predictinglocations of an occupant or an asset, or a tag associated with theoccupant or asset, within a structure.

SUMMARY

One embodiment includes a building control system. The building controlsystem includes a mobile tag operating to move within a structure, aplurality of sensors operating to sense a condition of the structure,and a controller. The controller operates to generate a first set ofweighted likelihoods based on a physical shape of the structure orcharacteristics of the structure, wherein the first set of weightedlikelihoods includes a weighted likelihood of the mobile tag being ateach one of a plurality of grid points within the structure, generate asecond set of weighted likelihoods based on the sensed condition of thestructure, wherein the second set of weighted likelihoods includes aweighted likelihood of the mobile tag being at each one of the pluralityof grid points within the structure, generate a combined set oflikelihoods based on the first set of weighted likelihoods and thesecond set of weighted likelihoods, and estimate a location of themobile tag within the structure based on the combined set oflikelihoods.

Another embodiment includes a method. The method includes identifying aphysical shape of a structure or characteristics of the structure,sensing, by a plurality of sensors, a second condition of the structure,generating a first set of weighted likelihoods based on the physicalshape of the structure or characteristics of the structure, wherein thefirst set of weighted likelihoods includes a weighted likelihood of amobile tag being at each one of a plurality of grid points within thestructure, generating a second set of weighted likelihoods based on thesensed condition of the structure, wherein the second set of weightedlikelihoods includes a weighted likelihood of the mobile tag being ateach one of the plurality of grid points within the structure,generating a combined set of likelihoods based on the first set ofweighted likelihoods and the second set of weighted likelihoods, andestimating a location of the mobile tag within the structure based onthe combined set of likelihoods.

Other aspects and advantages of the described embodiments will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a structure that includes a first set of sensors, a secondset of sensors, and a controller that estimates a location of a mobiletag based on sensed signals of the first and second sets of sensors,according to an embodiment.

FIG. 2 shows maps of weighted likelihoods for the first set of sensorsand for the second set of sensors, and a combined weighted likelihoodmap based on the maps of the weighted likelihoods for the first set ofsensors and for the second set of sensors.

FIG. 3 shows a structure, wherein the structure is characterized by gridpoints and another weighted likelihood map can be created by identifyingpossible paths of the mobile tag, according to an embodiment.

FIG. 4 shows a structure, wherein the structure is characterized byconvex shapes that include grid points, according to an embodiment.

FIG. 5 shows timelines that depict estimated probabilities of a tagbeing located at specific grid points over time, according to anembodiment.

FIG. 6 shows a structure, wherein the structure includes a tag thatcommunicates with a sensor of the structure, according to an embodiment.

FIG. 7 shows a sensor of the structure, according to an embodiment.

FIG. 8 shows a tag associated with an occupant of the structure,according to an embodiment.

FIG. 9 is a flow chart that includes steps of a method of estimatingweighted likelihood of a mobile tag being at grid points of a structure,according to an embodiment.

FIG. 10 is a flow chart that includes steps of a method of estimatingweighted likelihood of a mobile tag being at grid points of a structure,according to an embodiment.

DETAILED DESCRIPTION

The described embodiments are embodied in methods, apparatuses, andsystems for generating weighted likelihood maps for estimating alocation of a mobile tag. A first weighted likelihood map is generatedbased on sensed signals of a first type of sensor. A second weightedlikelihood map is generated based on sensed signals of a second type ofsensor. A combined set of likelihoods is generated based on the firstset of weighted likelihoods and the second set of weighted likelihoods.A location of the mobile tag within the structure is estimated based onthe combined set of likelihoods. Additional weighted likelihood maps canbe generated to improve the estimation of the location of the mobiletag. For an embodiment, grid points within a structure are identified,and weighted likelihoods of the tag being located at different gridpoints are estimated. For at least some embodiments, the weightedlikelihoods are supplemented with information associated with the tag.At least some embodiments include supplementing building control and/orbuilding intelligence with the estimated location of the mobile tag.

FIG. 1 shows a structure that includes a first set of sensors 121, 122,123, 124, 125, a second set of sensors 131, 132, 133, 134, 135, and acontroller 190 that estimates a location of a mobile tag 101 based onsensed signals of the first set of sensors 121, 122, 123, 124, 125, andthe second set of sensors 131, 132, 133, 134, 135, according to anembodiment. For an embodiment, the first set of sensors 121, 122, 123,124, 125 sense a first condition of the structure. For an embodiment,the second set of sensors 131, 132, 133, 134, 135 sense a secondcondition of the structure.

For an embodiment, the structure is represented by grid points. The gridpoints provide an overlay that section up the structure, wherein eachgrid point represents a different location within the structure. For anembodiment, the grid points are evenly-spaced throughout the structure.Exemplary grid points are shown in FIG. 1 as grid points 110, 111, 112,113, 114, 115, 116, 117, 118, 119. Note that in FIG. 1 many of the gridpoints do not have reference designators. As shown in FIG. 1, thestructure includes several rooms 140, 150, 160, 170, 180 which allinclude grid points.

For at least some embodiments, a controller 190 is connected to each ofthe first set of sensors 121, 122, 123, 124, 125, and the second set ofsensors 131, 132, 133, 134, 135. The connection between the controller190 and each sensor can include wired or wireless connections. For anembodiment, each sensor includes a wireless router, and the connectionbetween the controller and each of the sensors can include one or morewireless hops through one or more other sensors.

For at least some embodiments, the controller 190 operates to generate afirst set of weighted likelihoods based on the first sensed condition ofthe structure, wherein the first set of weighted likelihoods includes aweighted likelihood of the mobile tag 101 being at each one of aplurality of grid points within the structure. That is, based on thesensing by the first set of sensors, the controller generates a weightedlikelihood that the mobile tag 101 is at each of the grid points. For anembodiment, the first set of sensor includes motion sensors that eachsense motion within the structure. Based on the sensed motion, of eachof the first set of sensors, the controller generates a map of the gridpoints that includes a weighted likelihood that the mobile tag 101 islocated at each of the grid points.

Further, for at least some embodiments, the controller 190 operates togenerate a second set of weighted likelihoods based on the second sensedcondition of the structure, wherein the second set of weightedlikelihoods includes a weighted likelihood of the mobile tag 101 beingat each one of the plurality of grid points within the structure. Thatis, based on the sensing by the second set of sensors, the controllergenerates a weighted likelihood that the mobile tag is at each of thegrid points. For an embodiment, the second set of sensor includes RF(radio frequency) sensors that each sense wireless signals within thestructure. Based on the RF signals of each of the first set of sensors,the controller generates a map of the grid points that includes aweighted likelihood that the mobile tag 101 is located at each of thegrid points.

For at least some embodiments, at least one of the first set of sensors,the second set of sensors, or a third set of sensor includes acousticsensors. For an embodiment, sound vibrations generated by the mobile tagare sensed by the acoustic sensors. A time of flight can be estimated byknowing when the sound vibrations are generated and by knowing when thesound vibrations are sensed by the acoustic sensors. Based on a time offlight, the distance between the mobile tag and the acoustic sensors canbe estimated. A set of weighted likelihoods of the mobile tag 101 beingat each one of the plurality of grid points within the structure can begenerated by the distance estimates determined through the use of theacoustic sensors.

For at least some embodiments, after generating the first set ofweighted likelihoods and the second set of weighted likelihoods, thecontroller 190 operates to generate a combined set of likelihoods basedon the first set of weighted likelihoods and the second set of weightedlikelihoods. It is to be understood that the combined set of likelihoodscan include any number of possible sets of weighted likelihoods.

For at least some embodiments, the controller operates to estimate alocation of the mobile tag 101 within the structure based on thecombined set of likelihoods. That is, the combined set of likelihoodsincludes a weighted likelihood that the mobile tag is located at each ofthe grid points. The combined set of likelihoods is based on at leastthe weighted likelihoods of the first set of sensors and the weightedlikelihoods second set of sensors. The location of the mobile tag 101can be estimated based on the locations of the grid points correspondingwith the greatest weighted likelihoods of the combined set oflikelihoods.

FIG. 2 shows maps 210, 220 of weighted likelihoods for the first set ofsensors and for the second set of sensors, and a combined weightedlikelihood map 230 based on the maps 210, 220 of the weightedlikelihoods for the first set of sensors and for the second set ofsensors. The maps 210, 220, 230 of FIG. 2 show weighted likelihoods ofthe mobile tag being at grid points 110, 111, 112, 116, 117, 118 andother non-referenced grid points within the room 114 of the structure.

The exemplary map 210 shows a weighted likelihood that the mobile tag isat each of the grid points based on the sensed first condition of thestructure as sensed by the plurality of first sensors. This maprepresents a greater likelihood with more cross-hatch lines. That is,the weighted likelihood at the grid point 110 (no cross-hatching) isless than the weighted likelihood at the grid point 118 (morecross-hatching).

The exemplary map 220 shows a weighted likelihood that the mobile tag isat each of the grid points based on the sensed second condition of thestructure as sensed by the plurality of second sensors. Again, this maprepresents a greater likelihood with more crosshatch lines. That is, theweighted likelihood at the grid point 110 (no cross-hatching) is lessthan the weighted likelihood at the grid point 118 (morecross-hatching).

The exemplary map 230 shows the combined weighted likelihoods of theweighted likelihoods of the first sensed condition and the weightedlikelihoods of the second sensed condition. As previously described,additional weighted likelihood maps can be created and included in thedetermination of the combined weighted likelihoods map 230.

After the combined weighted likelihoods map 230 has been created, thelocation of the mobile tag can be estimated based on the grid points ofthe combined weighted likelihoods map 230 that indicate the greatestlikelihood.

FIG. 3 shows a structure, wherein the structure is characterized by thegrid points and another weighted likelihood map can be created byidentifying possible paths of the mobile tag 101, according to anembodiment. As shown, the exemplary structure includes the rooms 140,150, 160, 170, 180. Further, as shown, the structure includes the gridpoints (such as, grid points 110, 111, 112, 113, 114, 115, 116, 117,118, 119) at various locations within the structure. For at least someembodiments, an occupant or user of the mobile tag (for example, asdepicted at time t0) is located at a location (such as defined by gridpoint 110). For at least some embodiments, the occupant possesses (or isattached to) the previously described mobile tag 101. For descriptivepurposes, the term occupant and mobile tag may be used interchangeably.For at least some embodiments, the likelihood that the occupant islocated at another grid point in the future is estimated. Many factorscan be utilized in the estimation.

For an embodiment, the controller 190 associated with the structureperforms the estimating. While shown as a single controller 190, it isto be understood that the processing of the depicted controller 190 canbe performed by a distributed set of processors. Further, the processmay be performed remotely.

For an embodiment the controller 190 is operative to determine aninitial location of a tag at an initial time, determine a floor plan ofa structure, and estimate a probability (likelihood) that at a futurepoint in time that the tag is located at each of a plurality of gridpoints, wherein each of the plurality of grid points is associated witha different location within the structure. For at least someembodiments, estimating the probability (likelihood) that at the futurepoint in time that the tag is located at each of the plurality of gridpoints, includes identifying possible paths of the tag, estimating theprobability of the tag being at each of the plurality of grid pointsbased on the identified possible paths, a difference between the futurepoint in time and the initial time, and a distance between the initialposition and positions of each of the plurality of grid points.

For at least some embodiments, the controller 190 further operates togenerate the combined set of likelihoods based on the first set ofweighted likelihoods, the second set of weighted likelihoods, and thethird set of weighted likelihoods. That is, for example, a thirdweighted likelihood map is generated, and this third map is additionallyused in the determination of the combined set of likelihoods.

FIG. 4 shows a structure, wherein the structure is characterized byconvex shapes that include the grid points, according to an embodiment.As shown, the barriers or walls of the structure define areas in whichpossible paths of the occupant cannot cross. For an embodiment, thephysical barriers or walls of the structure define convex shapes 240,250, 260, 270, wherein each of the convex shapes includes groups of gridpoints. That is, for at least some embodiments, the total number of gridpoints within the structure and grouped into sub-groups forming aplurality of convex shapes (such as, convex shapes 240, 250, 260, 270).

Further, for at least some embodiments, estimating the probability ofthe tag being at each of the plurality of grid points includesestimating a probability the tag is within the convex shape associatedwith the grid point. As shown, for at least some embodiments, a shape ofeach of the plurality of convex shapes is defined by barriers of thestructure, and connecting points 225, 235, 245 between each of theplurality of convex shapes 240, 250, 260, 270 are defined by openingsbetween the barriers of the structure. Further, for at least someembodiments, each of the possible paths pass through the connectingpoints between the convex shapes. For an embodiment, the connectingpoints are used for identifying paths through the structure.

Utilizing convex shapes that include multiple grid points cansubstantially improve the processing needed to determine the probabilitythat the tag is at each of the plurality of grid points. That is,calculating the probability that the tag is at each of the plurality ofgrid points using information from all of the grid points takessubstantially more computational power than calculating the probabilitythat the tag is at each of the plurality of grid points usinginformation from convex shapes, thereby improving the processing ofcontroller that is operative to calculate the probability that the tagis at each of the plurality of grid points.

At least some embodiments include providing navigation between points(locations) of the structure. For at least some embodiments, knowledgeof grid points, convex shapes of the grid points, and/or connectingpoints between the convex shapes are used in determining navigationbetween locations of the structure. For an embodiment, the connectingpaths are utilized for determining a shortest path between locationpoints within the structure. For example, a user or a mobile computingdevice (mobile tag) of the user can submit a request to the controllerfor a shortest path between a present location of the user or the mobiledevice of the user, and a specified or desired location of the user. Foran embodiment, the controller uses the connecting points of the convexshapes of the grid points to identify the shortest path between thepresent location of the user and the desired or specified location ofthe user. Using the connecting points for the determination of theshortest path rather than all of the grid points substantially reducesthe processing of the controller.

That is, for an embodiment, the controller provides a user with ashortest path between points for navigation between two points. Asdescribed, the utilization of the connecting points between the convexshapes provides the ability to determine point to point navigation usingless processing than is all the grid points were to be utilized.

FIG. 5 shows timelines that depict the estimated weighted likelihood ofa tag being located at specific grid points over time, according to anembodiment. For at least some embodiments, estimating the weightedlikelihood that at the future point in time that the tag is located ateach of the plurality of grid points 113, 117, 118, include generatingthe first set of weighted likelihoods based on the first sensedcondition of the structure, wherein the first set of weightedlikelihoods includes a weighted likelihood of the mobile tag being ateach one of a plurality of grid points within the structure, generatinga second set of weighted likelihoods based on the second sensedcondition of the structure, wherein the second set of weightedlikelihoods includes a weighted likelihood of the mobile tag being ateach one of the plurality of grid points within the structure,generating a combined set of likelihoods based on the first set ofweighted likelihoods and the second set of weighted likelihoods. For atleast some embodiments, estimating the weighted likelihood that at thefuture point in time that the tag is located at each of the plurality ofgrid points 113, 117, 118, further includes identifying possible pathsof the tag, estimating the probability of the tag being at each of theplurality of grid points 113, 117, 118 based on the identified possiblepaths, a difference between the future point in time and the initialtime, and a distance between the initial position and positions of eachof the plurality of grid points 113, 117, 118.

FIG. 6 shows a structure, wherein the structure includes a tag 550 thatcommunicates with a sensor 410 and/or 412 of the structure, according toan embodiment. The communication between the tag and the sensor can befacilitated by any form of communication. For an embodiment, thecommunication includes electromagnetic waves, such as, radio frequency(RF) or optical waves.

For at least some embodiments, the sensed parameter is associated withthe tag 550. For at least some embodiments, the sensed parameterincludes an estimate of a quality of a wireless link between the tag 550and the one or more sensors 410, 412. For an embodiment, the one or moresensors include a transceiver, and the link quality includes a receivedsignal strength indicator (RSSI) between the transceiver and the mobiletag 550. For an embodiment, the RSSI is determined by signals receivedby the one or more sensors from the mobile tag 550. For an embodiment,the RSSI is determined by signals received by the mobile tag 550 fromthe one or more sensors. For an embodiment, a distance between themobile tag 550 and each of the sensors is approximated base on adifferent in signal power of transmitted signals relative to the RSSI.For an embodiment, the distance estimate between the mobile tag and eachof the sensors is used to estimate the location of the mobile tag. Foran embodiment, locations of each of the sensors is known, and thelocation of the mobile tag is estimated by triangulating using the knownlocations of the sensors and the estimated distance between each of thesensors and the mobile tag.

For at least some embodiments, at least one of the sensed parametersincludes sensed motion of the tag. For an embodiment, sensing motionincludes sensing whether are not the tag is moving. For an embodiment,sensing motion includes sensing a changing location of the tag ordetecting Significant Motion Detection of an Android virtual sensor. Foran embodiment, the mobile tag includes a pedometer.

For an embodiment, the sensors (such as sensors 410, 412) include amotion sensor. For an embodiment, the motion sensor includes a passiveinfrared (PIR) sensor. For at least some embodiments, the sensedparameter includes sensed ambient light.

For at least some embodiments, the sensed parameter includes sensedacceleration of the tag. For an embodiment, the acceleration is sensedby the tag itself. For example, the tag can include an accelerometerthat senses acceleration of the tag, which is then communicated to oneof the sensors (such as, sensor 410, 412). For an embodiment, theacceleration is sensed by an external device. That is, the accelerationof the mobile tag can be sensed externally from the tag by anothersensor.

For an embodiment, the acceleration is used to estimate orientation ofthe mobile tag with respect to gravity. Orientation of the mobile can beused to estimate expected RF strength due to antenna patterns and thisexpectation can be used to better calculate distance from a sensor and aprobability of the distance of the tag from the sensor. For at leastsome embodiments, a compass and/or a gyroscope are used for orientationwith respect to the earth, giving better orientation knowledge betweenone or more of the sensors and the mobile tag.

For at least some embodiments, orientation of the mobile tag providesinformation that can be used to determine how the peaks and nulls of theantenna patterns of the mobile tag alignment with one or more of thesensors. Accordingly, determinations of link qualities between thedevice and sensors can be more precisely determined and compensation forvarying orientations of the tag and the varying antenna patterns thatresult due to the varying orientation of the tag. For an embodiment, thecompensation improves estimates of the distance between the mobile tagand each the sensors, which can improve the location estimation of themobile tag.

For at least some embodiments, the sensed parameter includes sensedmotion of the tag, wherein the motion is sensed by the tag, andcommunicated to the one or more sensors. For at least some embodiments,the sensed parameter includes pedometer information from the tag. Themotion sensed by the mobile tag and/or the pedometer information can beused to estimate distances traveled by the user of the mobile tag,and/or directions traveled by the user of the mobile tag. For at leastsome embodiments, the sensed parameter includes directional (such as,magnetic) information from the tag. For at least some embodiments, thesensed parameter includes tag orientation. Tag orientation may bedelivered as a quaternion, Euler angles, or rotational matrix.

For at least some embodiments, the sensed parameter includes sensedmagnetic information from the tag. For at least some embodiments, thesensed magnetic information of the tag is utilized to generate magneticmapping of the structure. For at least some embodiments, the sensedmagnetic information is used to build a data base of a blueprint of thestructure.

FIG. 7 shows a sensor of the structure, according to an embodiment. Anembodiment of a smart sensor system 602 (which operate as the previouslydescribed sensors) includes a smart sensor CPU 635, a set of sensors640, and a communication interface 650. For an embodiment, anon-exhaustive list of sensors of the set of sensors 640 includes alight sensor 641, a motion sensor 642, a temperature sensor 643, acamera 644, and/or an air quality sensor 645. For an embodiment, thesmart sensor system 602 along with an environmental control manager 604provide and environmental control sub-system 600.

For at least some embodiments, one or more of the set of sensors 640 isused for sensing conditions within the structure for generating thefirst set of weighted likelihoods based on the first sensed condition ofthe structure, wherein the first set of weighted likelihoods includes aweighted likelihood of the mobile tag being at each one of a pluralityof grid points within the structure, and generating a second set ofweighted likelihoods based on the second sensed condition of thestructure, wherein the second set of weighted likelihoods includes aweighted likelihood of the mobile tag being at each one of the pluralityof grid points within the structure. As described, for at least someembodiments, the weighted likelihoods are used for estimating a locationof the mobile tag.

For at least some embodiments, the estimated locations of the mobile tagare used for controlling an environmental condition of the structure.That is, knowing the locations (or estimates of the locations) of mobiletags and the users associated with the mobile tags allows forintelligent control of the environment of the structure. For example,areas of the structure that do not include any occupants (users) canhave lights dimmed or turned off. Further, rooms with no occupants or alarge number of occupants can be temperature (through, for example, anHVAC (heating, ventilation, and air conditioning) system of thestructure) controlled accordingly.

For at least some embodiments, one or more of the set of sensors 640 areused for the sensing conditions which are additionally used to controlthe environment (for example, lighting control and or HVAC (heating,ventilation, and air conditioning) of the structure. That is, for anembodiment, the environment of the structure is controlled by both thepredicted location of the mobile tag (which typically include manymobile tags) and sensed conditions of the smart sensor system 602. Forexample, if a large number of mobile tags are identified to be locationwithin a common room of the structure, the temperature of the room canbe adjusted lower for comfort or energy savings. Additionally, oralternatively, the lighting of the room can be adjusted up or down.

The communication interface 650 of the smart sensor system provides acommunication channel for communicating with other smart sensors, withmobile tags, or with a backend server (such as, controller 190). Thecommunication can include RF (radio frequency) communication, such as,Wi-Fi or Bluetooth wireless communication.

The smart sensor CPU 635 provides intelligent control of the smartsensor system 602 by managing the communication and for some embodimentsproviding at least a portion of the location determination of the mobiletag(s).

The environmental control manager 604 which includes a managing CPU 620receives control information from the smart sensor system 603 andprovides control of an environmental control unit 646. For anembodiment, the environmental control unit 646 includes an HVAC(heating, ventilation, and air conditioning) system. For an embodiment,the environmental control unit 646 includes lighting control. For anembodiment, the environmental control unit 646 includes HVAC (heating,ventilation, and air conditioning) and lighting control.

FIG. 8 shows a mobile tag 700 associated with an occupant of thestructure, according to an embodiment. As previously described, for atleast some embodiments, the mobile tag 700 provides sensed informationthat can be additionally used to estimate a location of the mobile tag700 within the structure. For an embodiment, the sensed information ofthe mobile tag 700 is used to generate another set of weightedlikelihoods, wherein the set of weighted likelihoods includes a weightedlikelihood of the mobile tag 700 being at each one of a plurality ofgrid points within the structure.

For an embodiment, the sensed information of the mobile tag 700 iscommunicated to the controller 190, to aid in location determination ofthe mobile tag 700. For an embodiment, the mobile tag 700 communicateswith a sensor 410 which is connected to an upstream network thatincludes the controller 190.

As shown, for at least some embodiments, the mobile tag 700 includes acontroller 710 that manages the sensed information and managescommunication of the tag through, for example, a radio 775.

For at least some embodiments, a non-exhaustive list of sensors of themobile tag includes a GPS (global positioning system) receiver 720, apedometer 730, a camera 735, a motion detector 740, a microphone 750, acompass 770, a gyroscope 772, a barometric sensor 784, a thermometer774, and/or a light sensor 776.

Further, for at least some embodiments, the mobile tag 700 includes auser profile 760 which can include customized information associatedwith the user of the mobile tag 700. The customized information caninclude tendencies and preferences of the user which can be used tofurther aid the location estimation of the mobile tag, and/or can beused to communicate preferential environmental control information whichcan be used along with the location estimation of the tag to control theenvironment of the structure in which the mobile tag is located.

Further, the mobile tag 700 can include a user input 782 (such as, akeyboard or touchscreen) to allow a user of the mobile tag to providefeedback information or user preferences. The feedback information ofthe user can be used to validate or invalidate the location estimations.For an embodiment, the user feedback influences future locationestimations.

FIG. 9 is a flow chart that includes steps of a method of estimatingweighted likelihood of a mobile tag being at grid points of a structure,according to an embodiment. A first step 910 includes sensing, by aplurality of first sensors, a first condition of the structure. A secondstep 920 includes sensing, by a plurality of second sensors, a secondcondition of the structure. A third step 930 includes generating a firstset of weighted likelihoods based on the first sensed condition of thestructure, wherein the first set of weighted likelihoods includes aweighted likelihood of a mobile tag being at each one of a plurality ofgrid points within the structure. A fourth step 940 includes generatinga second set of weighted likelihoods based on the second sensedcondition of the structure, wherein the second set of weightedlikelihoods includes a weighted likelihood of the mobile tag being ateach one of the plurality of grid points within the structure. A fifthstep 950 includes generating a combined set of likelihoods based on thefirst set of weighted likelihoods and the second set of weightedlikelihoods. A sixth step 960 includes estimating a location of themobile tag within the structure based on the combined set oflikelihoods.

As previously described, for at least some embodiments, the firstplurality of sensors includes motion sensors, the second plurality ofsensors comprises RF sensors, and wherein the combined set oflikelihoods comprises an ensemble of the first set of weightedlikelihoods and the second set of weighted likelihoods.

As previously described, for at least some embodiments, the firstplurality of sensors comprises passive infrared (PIR) sensors and thesensed first condition of the structure comprises sensed motion of thestructure. As previously described, for at least some embodiments, thesecond plurality of sensors comprises wireless transceivers and thesensed second condition of the structure comprises a received signalstrength of wireless signals between the mobile tag and the secondplurality of sensors.

For an embodiment, the amount of motion sensed by the motion sensors(such as, the PIR sensor) influences the weighted likelihood. That is, amotion sensor that senses larger amounts of motion is more likely to beproximate to the mobile tag. Further, for an embodiment, sensed motionof different motion sensors is used to disambiguate between differentmobile tags. For an embodiment, a size of an asset associated with thetag influences the weighted likelihoods. That is, a larger asset maygenerate a larger sensed motion signal. The larger sensed motion due tothe size of the asset can be accounted for.

FIG. 10 is a flow chart that includes steps of a method of estimatingweighted likelihood of a mobile tag being at grid points of a structure,according to an embodiment. A first step 1010 includes determining aninitial location of the mobile tag at an initial time. A second step1020 includes estimating a likelihood that at a future point in timethat the mobile tag is located at each of the plurality of grid points,wherein each of the plurality of grid points is associated with adifferent location within the structure. For an embodiment, estimatingthe likelihood that at the future point in time that the mobile tag islocated at each of the plurality of grid points includes a third step1022 of identifying possible paths of the mobile tag, and a fourth step1024 of generating a third set of weighted likelihoods of the mobile tagbeing at each of the plurality of grid points based on the identifiedpossible paths, a difference between the future point in time and theinitial time, and a distance between the initial position and positionsof each of the plurality of grid points. For at least some embodiments,the grid points are equally spaced are substantially equally spacedthroughout at least a portion of the structure.

Further, at least some embodiments include generating the combined setof likelihoods based on the first set of weighted likelihoods, thesecond set of weighted likelihoods, and the third set of weightedlikelihoods.

For an embodiment, determining the initial location of the mobile tag atthe initial time includes identifying when the user of the mobile tagenters the structure. The initial location is the entry point of theuser.

For an embodiment, determining the initial location of the mobile tag atthe initial time includes selecting a location based on the RSSI,without considering building walls constraints. Room level locationconsistency is used to select the initial location. That is, the numberof times the RSSI indicates the tag is within a specific room of thestructure can be used as an initial location determination.

For an embodiment, determining the initial location of the mobile tag atthe initial time includes the user of the mobile device proactivelysending a message that indicates an initial location of the user of themobile tag. The initial location and the timing can be communicated, forexample, to the controller 190.

As previously described, for an embodiment, the controller furtheroperates to group the plurality of grid points into sub-pluralitiesforming a plurality of convex shapes, wherein each of thesub-pluralities of grid points define a convex shape within thestructure, and wherein estimating the likelihood of the tag being ateach of the plurality of grid points comprises estimating a likelihoodthe tag is within the convex shape associated with the grid point. Aspreviously described, for an embodiment, a shape of each of theplurality of convex shapes is defined by barriers of the structure andconnecting points between each of the plurality of convex shapes aredefined by openings between the barriers of the structure. As previouslydescribed, for an embodiment, each of the possible paths pass throughthe connecting points between convex shapes.

For at least some embodiments, the controller further operates togenerate a fourth set of weighted likelihoods based on a physical shapeof the structure or characteristics of the structure, and generate thecombined set of likelihoods based on the first set of weightedlikelihoods, the second set of weighted likelihoods, and the fourth setof weighted likelihoods. For at least some embodiments, the physicalshape of the structure includes the physical shape and characteristics(such as, unique wall structure and/or materials) of the structure. Forat least some embodiments, the physical shape of the structure includesthe fourth weighted likelihood of each grid points being influenced by aproximity of grid point to walls. For at least some embodiments, thephysical shape of the structure includes the fourth weighted likelihoodof each grid points being influenced by detection of multipath signalswithin the structure. For at least some embodiments, the physical shapeof the structure includes the fourth weighted likelihood of each gridpoints being influenced by determination of RF signal attenuation withinthe structure.

As previously described, for an embodiment, the controller furtheroperates to receive a parameter sensed by the mobile tag. As previouslydescribed, for an embodiment, the parameter includes sensed accelerationof the mobile tag. As previously described, for an embodiment, theparameter comprises pedometer information from the tag. As previouslydescribed, for an embodiment, the parameter comprises directional (forexample, magnetic) information from the tag. As previously described,for an embodiment, the parameter includes sensed magnetic informationfrom the tag. As previously described, for an embodiment, the sensedmagnetic information of the tag is utilized to generate magnetic mappingof the structure. As previously described, for an embodiment, themagnetic information is used to build a data base of a blueprint of thestructure.

While RSSI has been described as a method for determining distancesbetween the mobile tag and the sensors, for at least some embodiments,ambient light sensor and other combinations of sensors other thanRSSI/beacons are used for determining the distances. For an embodiment,a bi-direction BLE (Bluetooth enabled) radio of the magnetic tag isoperative to both send and receive beacons from the sensor. For at leastsome embodiments, the mobile tag transmits tag beacons which arereceived by the sensors. For at least some embodiments, transmit powerof the tag beacons is varied over time. Using lower transmit powerreduces the number of sensors that will receive the message, which canprovide greater location accuracy.

For an embodiment, a floor plan of the structure is either previouslyknown or is provided. For an embodiment, the floor plan in deduced, forexample, by the controller 190 over time by sensing or tracking motionof tags/occupants over time. Walls, for example, can be identifiedbecause mobile tags never travel through walls.

For an embodiment, motion sensors, such as, PIR (passive infrared)sensors are used to time synchronized the sensors which can be used toaid the previously described triangulation.

Although specific embodiments have been described and illustrated, thedescribed embodiments are not to be limited to the specific forms orarrangements of parts so described and illustrated. The embodiments arelimited only by the appended claims.

What is claimed:
 1. A building control system, comprising: a mobile tagoperating to move within a structure; a plurality of sensors operatingto sense a condition of the structure; and a controller, wherein thecontroller operates to: form a plurality of convex shapes by grouping aplurality of grid points into sub-pluralities of grid points, each ofthe sub-pluralities of grid points defining a convex shape within thestructure; generate a first set of weighted likelihoods based on aphysical shape of the structure or characteristics of the structure,wherein the first set of weighted likelihoods includes a weightedlikelihood of the mobile tag being at each one of a plurality of gridpoints within the structure; generate a second set of weightedlikelihoods based on the sensed condition of the structure, wherein thesecond set of weighted likelihoods includes a weighted likelihood of themobile tag being at each one of the plurality of grid points within thestructure; generate a combined set of likelihoods based on the first setof weighted likelihoods and the second set of weighted likelihoods;determine a distance between the mobile tag and the plurality of sensorsbased on at least one beacon received from the mobile tag by theplurality of sensors; and estimate a location of the mobile tag withinthe structure based on the combined set of likelihoods and the distancedetermined based on the at least one beacon.
 2. The system of claim 1,wherein the physical shape of the structure includes a unique wallstructure or materials of the structure.
 3. The system of claim 1,wherein the first set of weighted likelihoods is based on a proximity ofa grid point to a wall.
 4. The system of claim 1, wherein the first setof weighted likelihoods is based on detection of multipath signalswithin the structure.
 5. The system of claim 1, wherein the first set ofweighted likelihoods is based on determination of RF signal attenuationwithin the structure.
 6. The system of claim 1, wherein the structure ischaracterized by a convex shape based on at least one barrier of thestructure.
 7. The system of claim 6, wherein the at least one barrier ofthe structure include at least one physical barrier or wall of thestructure.
 8. The system of claim 1, wherein: a shape of each of theplurality of convex shapes is defined by barriers of the structure; andconnecting points between each of the plurality of convex shapes aredefined by openings or paths between the barriers of the structure. 9.The system of claim 1, wherein estimating the likelihood of the tagbeing at each of the plurality of grid points comprises estimating alikelihood the tag is within a convex shape associated with theplurality of grid points.
 10. A method, comprising: identifying aphysical shape of a structure or characteristics of the structureincluding: forming a plurality of convex shapes by grouping theplurality of grid points into sub-pluralities of grid points; and eachof the sub-pluralities of grid points defines a convex shape within thestructure; sensing, by a plurality of sensors, a condition of thestructure; generating a first set of weighted likelihoods based on thephysical shape of the structure or characteristics of the structure,wherein the first set of weighted likelihoods includes a weightedlikelihood of a mobile tag being at each one of a plurality of gridpoints within the structure; generating a second set of weightedlikelihoods based on the sensed condition of the structure, wherein thesecond set of weighted likelihoods includes a weighted likelihood of themobile tag being at each one of the plurality of grid points within thestructure; generating a combined set of likelihoods based on the firstset of weighted likelihoods and the second set of weighted likelihoods;determining a distance between the mobile tag and the plurality ofsensors based on at least one beacon received from the mobile tag by theplurality of sensors; and estimating a location of the mobile tag withinthe structure based on the combined set of likelihoods and the distancedetermined based on the at least one beacon.
 11. The method of claim 10,wherein identifying the physical shape of the structure orcharacteristics of the structure includes identifying a unique wallstructure or materials of the structure.
 12. The method of claim 10,wherein generating the first set of weighted likelihoods includesgenerating the first set of weighted likelihoods based on a proximity ofa grid point to a wall.
 13. The method of claim 10, wherein generatingthe first set of weighted likelihoods includes generating the first setof weighted likelihoods based on detection of multipath signals withinthe structure.
 14. The method of claim 10, wherein generating the firstset of weighted likelihoods includes generating the first set ofweighted likelihoods based on determination of RF signal attenuationwithin the structure.
 15. The method of claim 10, wherein identifyingthe physical shape of the structure or characteristics of the structureincludes characterizing a convex shape of the structure based on atleast one barrier of the structure.
 16. The method of claim 15, whereinthe at least one barrier of the structure include at least one physicalbarrier or wall of the structure.
 17. The method of claim 10, wherein:identifying the physical shape of the structure or characteristics ofthe structure includes defining a shape of each of the plurality ofconvex shapes based on barriers of the structure; and identifying thephysical shape of the structure or characteristics of the structureincludes defining connecting points between each of the plurality ofconvex shapes by openings or paths between the barriers of thestructure.
 18. The method of claim 10, wherein estimating the likelihoodof the tag being at each of the plurality of grid points comprisesestimating a likelihood the tag is within a convex shape associated withthe plurality of grid points.